Avances y aplicaciones de la programación lineal y no lineal difusa en la optimización de la agricultura moderna

Autores/as

DOI:

https://doi.org/10.17268/agroind.sci.2025.03.04

Palabras clave:

programación lineal difusa, optimización agrícola, gestión de recursos, lógica difusa, agricultura moderna

Resumen

El crecimiento poblacional ejerce una presión creciente sobre los sistemas agrícolas, demandando una gestión eficiente y sostenible de los recursos. La programación lineal difusa (PLD) ha emergido como una herramienta clave para abordar la incertidumbre en la asignación de recursos, optimizar patrones de cultivo y promover la sostenibilidad agrícola. El propósito de este artículo es revisar sistemáticamente los avances y aplicaciones de la programación lineal y no lineal difusa en la agricultura moderna, abarcando desde la gestión del agua y fertilizantes hasta la planificación de cultivos y la mitigación de impactos ambientales. Se identificaron 842 documentos mediante una búsqueda sistemática en bases de datos científicas, aplicando criterios de inclusión y exclusión para seleccionar estudios relevantes. Los hallazgos evidencian el potencial de la PLD para integrar múltiples objetivos y manejar incertidumbres inherentes al contexto agrícola, proporcionando soluciones prácticas y sostenibles. Sin embargo, persisten desafíos que limitan su adopción a gran escala.

Citas

Angammal, S., & Grace, G. (2024). Neutrosophic goal programming technique with bio inspired algorithms for crop land allocation problem. Scientific Reports, 14, 21565. https://doi.org/10.1038/s41598-024-69487-0

Biswas, A., & Pal, B. B. (2005). Application of fuzzy goal programming technique to land use planning in agricultural system. Omega, 33(5), 391-398. https://doi.org/10.1016/j.omega.2004.07.003

Bournaris, T., Papathanasiou, J., Moulogianni, C., & Manos, B. (2009). A fuzzy multicriteria mathematical programming model for planning agricultural regions. New Medit, 8(4), 22–27.

Chang, N. B., Chen, H. W., & Ning, S. K. (2001). Identification of river water quality using the fuzzy synthetic evaluation approach. Journal of Environmental Management, 63(3), 293–305. https://doi.org/10.1006/jema.2001.0483

Chen, J., Zhang, C., & Guo, P. (2022a). A credibility-based interval multi-objective crop area planning model for agricultural and ecological management. Agricultural Water Management, 269, 107687. https://doi.org/10.1016/j.agwat.2022.107687

Chen, Y., Zhou, Y., Fang, S., Li, M., Wang, Y., &, Cao, K. (2022b). Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty. Science of the Total Environment, 809, 151152. https://doi.org/10.1016/j.scitotenv.2021.151152

Cheng, Y., Jin, L., Pan, Y., Bai, R., Wei, Y., & Huang, G. (2022). An improved fuzzy sorting algorithm coupling bi-level programming for synergetic optimization of agricultural water resources: A case study of Fujian Province, China. Journal of environmental management, 312, 114946. https://doi.org/10.1016/j.jenvman.2022.114946

De, A., & Singh, S. P. (2021). Analysis of fuzzy applications in the agri-supply chain: A literature review. Journal of Cleaner Production, 283, 124577. https://doi.org/10.1016/j.jclepro.2020.124577

Ding, T., Steubing, B., & Achten, W. M. (2023). Coupling optimization with territorial LCA to support agricultural land-use planning. Journal of environmental management, 328, 116946. https://doi.org/10.1016/j.jenvman.2022.116946

Egea, G., Fernández, J. E., & Alcon, F. (2017). Financial assessment of adopting irrigation technology for plant-based regulated deficit irrigation scheduling in super high-density olive orchards. Agricultural Water Management, 87, 47-56. https://doi.org/10.1016/j.agwat.2017.03.008

Fernández, J. E., Alcon, F., Diaz-Espejo, A., Hernandez-Santana, V., & Cuevas, M. V. (2020). Water use indicators and economic analysis for on-farm irrigation decision: A case study of a super high density olive tree orchard. Agricultural water management, 237, 106074. https://doi.org/10.1016/j.agwat.2020.106074

Gui, Z., Li, M., & Guo, P. (2017). Simulation-based inexact fuzzy semi-infinite programming method for agricultural cultivated area planning in the Shiyang River Basin. Journal of Irrigation and Drainage Engineering, 143(2), 05016011. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001118

Guan, X., Mascaro, G., Sampson, D., & Maciejewski, R. (2020). A metropolitan scale water management analysis of the food-energy-water nexus. Science of the Total Environment, 701, 134478. https://doi.org/10.1016/j.scitotenv.2019.134478

Ilbahar, E., Kahraman, C., & Cebi, S. (2021). Location selection for waste-to-energy plants by using fuzzy linear programming. Energy, 234, 121189. https://doi.org/10.1016/j.energy.2021.121189

Jana, C., & Chattopadhyay, R. N. (2005). Direct energy optimization for sustainable agricultural operation-a fuzzy linear programming approach. Energy for Sustainable Development, 9(3), 5-12. https://doi.org/10.1016/S0973-0826(08)60517-7

Kang, S., Hao, X., Du, T., Tong, L., Su, X., Lu, H., ... & Ding, R. (2017). Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice. Agricultural Water Management, 179, 5-17. https://doi.org/10.1016/j.agwat.2016.05.007

Kamber, E., Aydoğmuş, U., Aydoğmuş, H. Y., Gümüş, M., & Kahraman, C. (2024). Prioritization of drip-irrigation pump alternatives in agricultural applications: an integrated picture fuzzy BWM&CODAS methodology. Applied Soft Computing, 154, 111308. https://doi.org/10.1016/j.asoc.2024.111308

Karmakar, S., Seikh, M. R., & Castillo, O. (2021). Type-2 intuitionistic fuzzy matrix games based on a new distance measure: Application to biogas-plant implementation problem. Applied Soft Computing, 106, 107357. https://doi.org/10.1016/j.asoc.2021.107357

Khare, D., & Jat, M. K. (2006). Assessment of counjunctive use planning options: A case study of Sapon irrigation command area of Indonesia. Journal of Hydrology, 328(3-4), 764-777. https://doi.org/10.1016/j.jhydrol.2006.01.018

Kousar, S., Zafar, A., Kausar, N., Pamucar, D., & Kattel, P. (2022). Fruit production planning in semiarid zones: a novel triangular intuitionistic fuzzy linear programming approach. Mathematical Problems in Engineering, 2022(1), 3705244. https://doi.org/10.1155/2022/3705244

Jacobs, K., Lebel, L., Buizer, J., Addams, L., Matson, P., McCullough, E., ... & Finan, T. (2016). Linking knowledge with action in the pursuit of sustainable water-resources management. Proceedings of the National Academy of Sciences, 113(17), 4591-4596. https://doi.org/10.1073/pnas.0813125107

Laskookalayeh, S. S., Najafabadi, M. M., & Shahnazari, A. (2022). Investigating the effects of management of irrigation water distribution on farmers' gross profit under uncertainty: A new positive mathematical programming model. Journal of Cleaner Production, 351, 131277. https://doi.org/10.1016/j.jclepro.2022.131277

Li, Y. P., Huang, G. H., Wang, G. Q., & Huang, Y. F. (2009). FSWM: a hybrid fuzzy-stochastic water-management model for agricultural sustainability under uncertainty. Agricultural water management, 96(12), 1807-1818. https://doi.org/10.1016/j.agwat.2009.07.019

Li, M., Fu, Q., Singh, V. P., & Liu, D. (2018). An interval multi-objective programming model for irrigation water allocation under uncertainty. Agricultural Water Management, 196, 24-36. https://doi.org/10.1016/j.agwat.2017.10.016

Li, M., Fu, Q., Singh, V. P., Ma, M., & Liu, X. (2017). An intuitionistic fuzzy multi-objective non-linear programming model for sustainable irrigation water allocation under the combination of dry and wet conditions. Journal of Hydrology, 555, 80-94. https://doi.org/10.1016/j.jhydrol.2017.09.055

Li, X., Huang, G., Wang, S., Li, Y., Zhang, X., & Zhou, X. (2022). An interval two-stage fuzzy fractional programming model for planning water resources management in the coastal region–A case study of Shenzhen, China. Environmental Pollution, 306, 119343. https://doi.org/10.1016/j.envpol.2022.119343

Lu, H., Huang, G., & He, L. (2011). An inexact rough-interval fuzzy linear programming method for generating conjunctive water-allocation strategies to agricultural irrigation systems. Applied Mathematical Modelling, 35(9), 4330-4340. https://doi.org/10.1016/j.apm.2011.03.008

Lu, H. W., Huang, G. H., & He, L. (2012). Simulation-based inexact rough-interval programming for agricultural irrigation management: a case study in the Yongxin County, China. Water resources management, 26, 4163-4182. https://doi.org/10.1007/s11269-012-0138-6

Liu, M., Huang, G. H., Liao, R. F., Li, Y. P., & Xie, Y. L. (2013). Fuzzy two-stage non-point source pollution management model for agricultural systems—A case study for the Lake Tai Basin, China. Agricultural Water Management, 121, 27-41. https://doi.org/10.1016/j.agwat.2013.01.006

Naciones Unidas (2025). Una población en crecimiento. https://www.un.org/es/global-issues/population

Nematian, J. (2023). A Two-Stage Stochastic Fuzzy Mixed-Integer Linear Programming Approach for Water Resource Allocation under Uncertainty in Ajabshir Qaleh Chay Dam. Journal of Environmental Informatics, 41(1). https://doi.org/10.3808/jei.202300487

Ning, S. K., & Chang, N. B. (2004). Optimal expansion of water quality monitoring network by fuzzy optimization approach. Environmental Monitoring and Assessment, 91, 145-170. https://doi.org/10.1023/B:EMAS.0000009233.98215.1f

Niquin-Alayo, E., Vergara-Moreno, E., & Calderón-Niquín, M. (2018). FERTIDIF: software para la planificación de fertilización agrícola basado en optimización lineal con costos difusos. Scientia Agropecuaria, 9(1), 103-112. https://doi.org/10.17268/sci.agropecu.2018.01.11

Ou, G., Tan, S., Zhou, M., Lu, S., Tao, Y., Zhang, Z., ... & Wu, G. (2017). An interval chance-constrained fuzzy modeling approach for supporting land-use planning and eco-environment planning at a watershed level. Journal of environmental management, 204, 651-666. https://doi.org/10.1016/j.jenvman.2017.09.021

Pan, X. H., Wang, Y. M., He, S. F., Labella, Á., & Martínez, L. (2023). An interval type-2 fuzzy ORESTE method for waste-to-energy plant site selection: a case study in China. Applied Soft Computing, 136, 110092. https://doi.org/10.1016/j.asoc.2023.110092

Perez, R. R., Batlle, V. F., & Rodriguez, L. S. (2007). Robust system identification of an irrigation main canal. Advances in Water Resources, 30(8), 1785-1796. https://doi.org/10.1016/j.advwatres.2007.02.002

Qadir, M., Sharma, B. R., Bruggeman, A., Choukr-Allah, R., & Karajeh, F. (2007). Non-conventional water resources and opportunities for water augmentation to achieve food security in water scarce countries. Agricultural water management, 87(1), 2-22. https://doi.org/10.1016/j.agwat.2006.03.018

Raju, K. S., & Duckstein, L. (2003). Multiobjective fuzzy linear programming for sustainable irrigation planning: an Indian case study. Soft Computing, 7(6), 412-418. https://doi.org/10.1007/s00500-002-0230-6

Ren, C., Guo, P., Tan, Q., & Zhang, L. (2017). A multi-objective fuzzy programming model for optimal use of irrigation water and land resources under uncertainty in Gansu Province, China. Journal of Cleaner Production, 164, 85-94. https://doi.org/10.1016/j.jclepro.2017.06.185

Rodríguez, E. R. V., & Martínez-López, Y. (2019). Decision-Making in Agriculture with the Use of Fuzzy Mathematical Models. Revista Ciencias Técnicas Agropecuarias, 28(2), 1-6. https://revistas.unah.edu.cu/index.php/rcta/article/view/1121

Rouyendegh, B. D., & Savalan, Ş. (2022). An integrated fuzzy MCDM hybrid methodology to analyze agricultural production. Sustainability, 14(8), 4835. https://doi.org/10.3390/su14084835

Rong, Q., Cai, Y., Su, M., Yue, W., Dang, Z., & Yang, Z. (2019). Identification of the optimal agricultural structure and population size in a reservoir watershed based on the water ecological carrying capacity under uncertainty. Journal of Cleaner Production, 234, 340-352. https://doi.org/10.1016/j.jclepro.2019.06.179

Srivastava, P., & Singh, R. M. (2015). Optimization of cropping pattern in a canal command area using fuzzy programming approach. Water Resources Management, 29, 4481-4500. https://doi.org/10.1007/s11269-015-1071-2

Tang, Y., Zhang, F., Wang, S., Zhang, X., Guo, S., & Guo, P. (2019). A distributed interval nonlinear multiobjective programming approach for optimal irrigation water management in an arid area. Agricultural water management, 220, 13-26. https://doi.org/10.1016/j.agwat.2019.03.052

Vergara, E. R., & Neyra Salvador, C. (2021). Fuzzy model and method for farmland fertilization planning. Selecciones Matemáticas, 8(02), 370-378. https://doi.org/10.17268/sel.mat.2021.02.13

Wang, Y., Guo, S. S., & Guo, P. (2022). Crop-growth-based spatially-distributed optimization model for irrigation water resource management under uncertainties and future climate change. Journal of Cleaner Production, 345, 131182. https://doi.org/10.1016/j.jclepro.2022.131182

Yani, M., Asrol, M., Hambali, E., Papilo, P., Mursidah, S., & Marimin, M. (2022). An adaptive fuzzy multi-criteria model for sustainability assessment of sugarcane agroindustry supply chain. IEEE Access, 10, 5497-5517. https://doi.org/10.1109/ACCESS.2022.3140519

Yang, G., Guo, P., Li, M., Fang, S., & Zhang, L. (2016). An improved solving approach for interval-parameter programming and application to an optimal allocation of irrigation water problem. Water resources management, 30, 701-729. https://doi.org/10.1007/s11269-015-1186-5

Yue, Q., & Guo, P. (2021). Managing agricultural water-energy-food-environment nexus considering water footprint and carbon footprint under uncertainty. Agricultural Water Management, 252, 106899. https://doi.org/10.1016/j.agwat.2021.106899

Yuan, G. Q., Liu, Y. H., & Cao, C. (2011). Fuzzy crop production planning expected value model with credibility constraints. Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 1, 630-634. IEEE. https://doi.org/10.1109/FSKD.2011.6019531

Zhang, X., Huang, G. H., & Nie, X. (2009). Optimal decision schemes for agricultural water quality management planning with imprecise objective. Agricultural Water Management, 96(12), 1723-1731. https://doi.org/10.1016/j.agwat.2009.07.011

Zhang, Y., & Huang, G. (2010). Fuzzy robust credibility-constrained programming for environmental management and planning. Journal of the Air & Waste Management Association, 60(6), 711-721. https://doi.org/10.3155/1047-3289.60.6.711

Zhang, Y. M., & Huang, G. H. (2011). Inexact credibility constrained programming for environmental system management. Resources, Conservation and Recycling, 55(4), 441-447. https://doi.org/10.1016/j.resconrec.2010.11.007

Zhang, Y. M., Lu, H. W., Nie, X. H., He, L., & Du, P. (2014). An interactive inexact fuzzy bounded programming approach for agricultural water quality management. Agricultural Water Management, 133, 104-111. https://doi.org/10.1016/j.agwat.2013.11.003

Zhang, C., & Guo, P. (2018). FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation. Agricultural Water Management, 199, 105-119. https://doi.org/10.1016/j.agwat.2017.12.013

Zhang, C., Yang, G., Wang, C., & Huo, Z. (2023). Linking agricultural water-food-environment nexus with crop area planning: a fuzzy credibility-based multi-objective linear fractional programming approach. Agricultural Water Management, 277, 108135. https://doi.org/10.1016/j.agwat.2022.108135

Zuo, Q., Wu, Q., Yu, L., Li, Y., & Fan, Y. (2021). Optimization of uncertain agricultural management considering the framework of water, energy and food. Agricultural Water Management, 253, 106907. https://doi.org/10.1016/j.agwat.2021.106907

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Publicado

2025-09-29

Cómo citar

Asis-Lopez, M., Hilasaca-Condori, J., & Huamán-Romero, P. (2025). Avances y aplicaciones de la programación lineal y no lineal difusa en la optimización de la agricultura moderna. Agroindustrial Science, 14(3), 229-242. https://doi.org/10.17268/agroind.sci.2025.03.04

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